March 23, 2014

Houston, Dallas, Columbus and Toronto are the biggest early season movers. All four clubs have moved up about 30 points in the Elo ratings so far this year. On the flip side, New York, Portland, New England, Montreal, and DC United have underperformed, moving backwards about 30 points so far. No, you didn't misread that, even though DC United started the season over 200 points below average, they have managed to move down an additional 26 points this year. The lowest rating any team has ever had in my Elo system is a 1251 by the Metrostars in August 1999 - DC United is dangerously close to breaking that record.

Throughout the season, I'll especially be looking at Toronto's progress. With the addition of Michael Bradley and Jermaine Defoe, they've had one of the biggest offseasons in league history, and results so far show those changes paying dividends. My rater still weights their quality very heavily based on 2013 results, but the predictions are swinging relatively quickly on just two 2014 results. This weekend will be especially interesting - if they can pick up a result in Salt Lake City, their Elo Rating and End of Season Points Predicted will skyrocket.

Right now, my predictions are showing Salt Lake as 4.7 point favorites for the Supporters Shield, but of course the season is still very young.

On the Monte Carlo side of the numbers, the biggest change we see is Salt Lake's Supporters Shield chances dropping almost 14 percentage points. This has little to do with their draw against Los Angeles and much to do with Kansas City and Houston getting wins.The way the Monte Carlo numbers were looking at things - no one in the East was threatening a Supporters Shield run (early as these numbers are), so whoever won the West essentially won the Supporters Shield as well. After two of the strongest East teams picked up points, it made the Supporters Shield race significantly more competitive. Now, again, these numbers are still extremely young, but it is interesting to follow them over the course of a season to see how they react on a week to week basis.

Toronto, Montreal, Chivas, and DC all have yet to register as having a chance at the Supporters Shield. This obviously doesn't mean they don't have a chance to win it, it just means their estimation is so low it might as well be zero. In the case of Toronto, this is most likely severely underestimated, and in a few more results they will start to show in that column. In the case of Montreal, Chivas, and DC, these teams are all rated very poorly, so unless they start churning out consistent big results, they will most likely never make it to that column.

The same train of thought goes for the playoff probability column. However, the playoffs are obviously easier to make than winning the Supporters Shield, so we'll expect quite a bit more variation here. I'll mostly be keeping an eye on Toronto in the SS and PO columns, as I really don't expect Montreal, Chivas, or DC to be Supporters Shield (or even playoff) contenders this year.

Week 1

Here is the change from the end of season after the first week of games. Colorado, San Jose, and Toronto have not yet played.

After one game...

This is how much the end of season Elo and Monte Carlo have changed after just one game:

Monte Carlo Estimation

Last season I began work on a Monte Carlo estimation that looked at the rest of the MLS schedule and predicted where each team would finish based on my Elo ratings of each team. If you are familiar with Sports Club Stats, I am doing pretty much the exact same thing as they are, the only difference is I am using W/L/D probabilities based on my Elo ratings rather than a league average home win percentage like Sports Club Stats uses.

Now, one of the unique things I don't do with my Elo ratings is reset or adjust them at the end of each season. I see (for better or worse) an Elo rating as a self-correcting system, so if a team's form significantly changes because of offseason moves, managerial changes, etc., the model will correct their rating based on new results. This has a very interesting effect on the Monte Carlo predictions. In fact, one could go so far as to say it breaks the Monte Carlo predictions - skewing them so hard to the last year's form that they are hardly meaningful. Let's take a look at the Monte Carlo estimations to see what I mean:

These are the estimations for year end 2014 before a single game has been played. Right now Chivas USA is predicted to have no chance of making the playoffs and DC United has about a 1/10,000 chance. Perhaps the most disturbing prediction here is Toronto; even after acquiring Michael Bradley and Jermaine Defoe (which this model has no way of taking into consideration) they have less than a 1% chance to make the playoffs. On the top of the table, the 2013 MLS Cup finalists Real Salt Lake and Sporting Kansas City are virtually guaranteed playoff spots.

There are positives and negatives to this. On one hand, MLS is a league of parity and we've seen many teams go from bottom dwellers to cup contenders in a single season (or vice versa). On the other hand, does anyone really expect Chivas USA to compete for a playoff spot this year? Even the odds makers have them at 1/66 chance of winning MLS Cup before a single game has been played.

My Monte Carlo numbers are an extreme. What they are doing is essentially inserting a carbon copy of the teams we witnessed last year and playing out an entire season between those carbon copies. Sports Club Stats' numbers are the polar opposite. They are erasing all history and assigning every team a 50% or 55.6% chance - based on conference - of making the playoffs. Reality is probably somewhere in between. It's hard to claim that Chivas USA has a 55.6% chance of making the playoffs in 2014. In fact, when you compare the two predictions, 55.6% or 0%, most people would probably agree that 0% is the more likely number.

But at the same time, Chivas is a special example. Even talking about the two other teams that had disastrous 2013 seasons - Toronto and DC - my predictions seem low. A lot of that has to do with Toronto and DC's offseason revamping. But, it's not a stretch to assume that teams coming off a disastrous season are going to make a lot of changes, and that's something this model doesn't assume. Like my Elo ratings themselves, these numbers will only reflect the change in form after a few 2014 games have been played.

Personally, I like to look at these along side Sport Clubs Stats' playoff chances and use the two as a range. I do think that using a form rating is a better way to run Monte Carlo estimations than league average home win percentages, but form rating is in itself a subjective (at least partially) measure. So, take these with a grain of salt, if you take them at all. But towards the end of the season when form is being determined by 2014 results - that's when this method shines.

Field Size and Orientation

Sources:
www.wikipedia.org
maps.google.com

Regular Season vs. US Open Cup

College Football Promotion and Relegation

Disclaimer: I have never watched a single college football game in my life. The following work isn't me on a soapbox or me pushing for an overhaul of the current college football format: it's just me dorking around. That's all.



For shits and giggles, I wanted to implement a promotion and relegation system to American college football. There are so many teams that it lends itself quite nicely to a pro/rel system. Since the season lasts about 14 weeks, I decided I wanted to create leagues of eight teams - playing each opponent in your league home and away would give you 14 games. Because there are so many teams, I decided to split divisions regionally immediately after the top flight. My league structure looks like this:

1) Every league has eight teams
2) Each division has twice as many leagues as its preceding division (i.e. Div I has one league, Div II has two leagues, Div III has four leagues, etc.)
3) Every league promotes one team at the end of the season
4) Every league relegates two teams at the end of the season
5) Teams are moved in a way that attempts to maintain divisional regions

To rank the teams against one another (since teams will rarely if ever actually be playing their league opponents in real life), I'll be using Jeff Sagarin's college football ratings. I like the work he does with Major League Soccer ratings, and unlike the BCS ratings or AP ratings, he rates ALL the Div I-A and Div I-AA teams, not just the top 25. Debate amongst yourselves how good or bad these ratings are; I have to use some kind of rating system, these are some of the best I've seen, and they are very easy to obtain.

The initial league set-up will look like this:

Division I: Teams ranked 1-8, one league
Division II: Teams ranked 9-24, two leagues split regionally
Division III: Teams ranked 25-56, four leagues split regionally
Division IV: Teams ranked 57-120, eight leagues split regionally

Using Sagarin's rankings as of December 14, 2013.

This year, all I'm doing is setting the leagues up. Next year, I'll promote and relegate teams based on Saragin's end-of-season rankings. Since I'm stopping at four divisions (simply due to the amount of time it takes), I'll replace all the relegated Division IV teams with the highest-ranked teams that I didn't draw into these leagues. Essentially what this does is creates a "pool" of all the teams that didn't make the top 120 this year, and the top 16 of the sub-120s will be promoted and sorted by region. This is structurally different than what would happen if I were to continue on with a Div V, VI, etc, but I have to sleep sometime. Hypothetically, I could continue creating divisions to incorporate NCAA Div II and Div III as well.

Parsing the teams by rank and region gives me the following division/league structures:

Division I:

Division II:

Division III:

Division IV:

Now I wait a year to figure out what happens.